AI Revolution in Traffic Management: India's Innovative Vehicle Detection Model

Researchers at NIT, Rourkela, have developed the AI-based MCVD model and LFBFPN tool to enhance traffic management in developing nations. Led by Santos Kumar Das, the project tackles mixed traffic detection challenges using advanced AI techniques. The findings have been published in the IEEE Transactions on Intelligent Transportation Systems.

The National Institute of Technology in Rourkela has unveiled a groundbreaking AI-driven model designed to revolutionize traffic management in developing countries. The project, spearheaded by Santos Kumar Das, focuses on mixed-traffic environments prevalent in nations like India, where diverse vehicles share road space.

The Multi-Class Vehicle Detection (MCVD) model, alongside the Light Fusion Bi-Directional Feature Pyramid Network (LFBFPN) tool, aims to improve real-time traffic data collection for optimizing flow and reducing congestion. Traditional methods fall short in adverse weather conditions and mixed-traffic scenarios, necessitating this innovative approach.

Utilizing deep learning and convolutional neural networks, the MCVD model has demonstrated superior accuracy in vehicle detection, tested on the Heterogeneous Traffic Labelled Dataset and Nvidia Jetson TX2. The team plans to commercialize this technology, paving the way for improved traffic control systems and road safety in developing regions.

(With inputs from agencies.)